Introduction to Operations Research, Volume 1CD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |
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Page 408
The arcs of a network may have a flow of some type through them , e.g. , the flow of trams on the roads of Seervada Park in Sec . 9.1 . Table 9.1 gives several examples of flow in typical networks . If flow through an arc is allowed in ...
The arcs of a network may have a flow of some type through them , e.g. , the flow of trams on the roads of Seervada Park in Sec . 9.1 . Table 9.1 gives several examples of flow in typical networks . If flow through an arc is allowed in ...
Page 422
Maximize the flow of oil through a system of pipelines . 4. Maximize the flow of water through a system of aqueducts . 5. Maximize the flow of vehicles through a transportation network . For some of these applications , the flow through ...
Maximize the flow of oil through a system of pipelines . 4. Maximize the flow of water through a system of aqueducts . 5. Maximize the flow of vehicles through a transportation network . For some of these applications , the flow through ...
Page 429
Employing the equations given in the bottom right - hand corner of the figure , these flows then are used to calculate the net flow generated at each of the nodes ( see columns H and I ) . These net flows are required to be 0 for the ...
Employing the equations given in the bottom right - hand corner of the figure , these flows then are used to calculate the net flow generated at each of the nodes ( see columns H and I ) . These net flows are required to be 0 for the ...
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity algebraic algorithm allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables dual problem dynamic programming entering basic variable example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal goal programming graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero